Forecasting ATM cash demands

In Singapore, customers of banking giant DBS conduct 25 million transactions a month at more than 1,100 ATMs. They rely on DBS – the island nation's largest bank – for convenient, ready access to funds day or night. To make sure that its ATMs – and its customers – don't come up empty-handed (i.e., a cash-out), DBS uses SAS to forecast withdrawal activity and to optimize the reloading process.

As a result, more than 30,000 hours of customer wait time have been eliminated annually as customers are spared the inconvenience of waiting while empty ATMs are reloaded.

Using this innovative solution, a first in the banking world, DBS is now able to convert valuable ATM usage and customer behavior data into a daily execution plan, allowing optimal reloading at non-peak times.

David Gledhill
Managing Director and Head of Group Technology and Operations

"We serve over 4 million customers in Singapore, and it is important for us to place customers at the heart of the banking experience across all our touch points," says David Gledhill, Managing Director and Head of Group Technology and Operations at DBS Bank.

"DBS' ATMs have one of the highest utilization rates in the world. Any downtime in a single ATM would mean inconvenience for our customers. Hence, we have to continually improve the efficiency of our ATM network and operational process."

DBS analyzes withdrawal data from each ATM to forecast upcoming activity. The forecasts allow the bank to make smarter decisions about reloading its network of machines.

The goal is to stock each ATM with just enough cash to avoid running out – with as few reloading trips as possible.

"There were clearly opportunities to improve the previous reloading process," Gledhill says. "The ATMs would either run out of cash before the scheduled reload or the ATM reloading personnel would drive out to the machines to put in more cash during peak usage period for that specific location, such as during lunch hour in the business district."

Cash-outs down 80 percent

With SAS Analytics and the determination to deliver excellent customer service with greater efficiency, DBS has optimized operations for its entire network of over 1,100 ATMs in Singapore.

The results:

Cash-outs (i.e., empty ATMs) are down by more than 80 percent.

More than 30,000 hours of customer wait time have been eliminated.

The number of trips required to reload the network is down by 20 percent.

The amount of leftover cash returned to the bank has decreased by more than 40 percent.

1,100 ATMs with optimized operations.

20 percent reduction in trips to replenish the network.

40 percent reduction in cash sent back to the bank.

80 percent reduction in cash-outs.

4 million customers spared inconvenience.

Innovation arises from challenge

Determined to treat the problem at its root, DBS embarked on a journey to place customers at the heart of the banking experience – and to formulate a long-term solution that brings intelligence into current processes. The aim was to move away from reactive measures and to move into a preemptive model that will lower the occurrence of undesirable situations through forecasting, which will in turn improve the overall customer experience.

The objective was to determine how forecasting and optimization analytics could improve on the efficiency of the bank's network or its customers' experience.

With that in mind, DBS partnered with SAS to develop and implement an innovative solution that seamlessly integrates disparate operating concepts from manufacturing and logistics industries, as well as operations-research techniques such as forecasting, optimization and queuing theory to optimize cash loading in one of the world's busiest ATM networks.

The success of the implementation was dependent on two fundamental aspects. First – and perhaps more important – was getting the forecast right, followed by developing and executing the optimization model.

Nimish Panchmatia, Executive Director and Head of Singapore Consumer Banking Operations, was the lead on this project. "We set out to accurately assess customers' withdrawal patterns across the entire network for each machine," he says. "Using these forecasts, we were able to generate an optimized schedule that achieved minimum cash-outs and trips while being operationally realistic and robust."

A pilot using a limited number of machines built a significant business case among measures including cash-outs, wait times, availability and reloading costs.

An optimized network

With accurate forecasts of withdrawal patterns at each ATM, the bank developed an optimization model that produced a replenishment schedule for the entire network. The optimization engine consumed the forecasting results as input that indicated when cash-outs were likely to occur. However, the model's real strength was its ability to minimize both the number of possible cash-outs and the number of trips required to replenish the network.

At the same time, it reduced the amount of cash returned from each replenished machine.

"This is an incredible milestone in our business analytics journey to help strengthen our business processes, increase operational efficiency and drive service excellence through analytics," says Gledhill.

"Prior to using the SAS solution, we focused our efforts on tackling problems whenever they arose and setting rigid alerts to prevent similar occurrences from happening again. Now, with accurate forecasting and high-performance analytics, we are able to predict when and where a problem may arise in real time," says Gledhill. "This gives us more flexibility to work on optimizing our strategies and deploying resources to continuously deliver the best experience to our customers.

"Using this innovative solution – a first in the banking world – DBS is now able to convert valuable ATM usage and customer behavior data into a daily, weekly and even monthly execution plan, allowing for optimal reloading at non-peak times."

DBS continues to embark on its journey to innovate and establish the finest high-performance analytics framework within the bank. Today, SAS solutions are used by departments across the firm to provide real-time analysis of customer, credit and operational analytics. The scalability of its system will continue to allow DBS to provide the best services to its increasing customer base and to be constantly regarded as a leading financial institution in Asia.

Challenge

Solution

Benefits

Accurate forecasts of withdrawal patterns at each machine result in: a 10% reduction in trips to replenish the network, a 33% improvement in the amount of cash sent back to the bank after replenishment, a 80% reduction in cash-outs and more than 350,000 customers a year spared inconvenience.

The results illustrated in this article are specific to the particular situations, business models, data input, and computing environments described herein. Each SAS customer’s experience is unique based on business and technical variables and all statements must be considered non-typical. Actual savings, results, and performance characteristics will vary depending on individual customer configurations and conditions. SAS does not guarantee or represent that every customer will achieve similar results. The only warranties for SAS products and services are those that are set forth in the express warranty statements in the written agreement for such products and services. Nothing herein should be construed as constituting an additional warranty. Customers have shared their successes with SAS as part of an agreed-upon contractual exchange or project success summarization following a successful implementation of SAS software. Brand and product names are trademarks of their respective companies.